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Layers of Experiments with Adaptive Combined Design

Author

Listed:
  • Sungil Kim
  • Heeyoung Kim
  • Jye‐Chyi Lu
  • Michael J. Casciato
  • Martha A. Grover
  • Dennis W. Hess
  • Richard W. Lu
  • Xin Wang

Abstract

In the field of nanofabrication, engineers often face unique challenges in resource‐limited experimental budgets, the sensitive nature of process behavior with respect to controllable variables, and highly demanding tolerance requirements. To effectively overcome these challenges, this article proposes a methodology for a sequential design of experiments through batches of experimental runs, aptly named Layers of Experiments with Adaptive Combined Design (LoE/ACD). In higher layers, where process behavior is less understood, experimental regions cover more design space and data points are more spread out. In lower layers, experimental regions are more focused to improve understanding of process sensitivities in a local, data‐rich environment. The experimental design is a combination of a space‐filling and an optimal design with a tuning parameter that is dependent on the amount of information accumulated over the various layers. The proposed LoE/ACD method is applied to optimize a carbon dioxide (epet‐CO2) assisted deposition process for fabricating silver nanoparticles with pressure and temperature variables. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 127–142, 2015

Suggested Citation

  • Sungil Kim & Heeyoung Kim & Jye‐Chyi Lu & Michael J. Casciato & Martha A. Grover & Dennis W. Hess & Richard W. Lu & Xin Wang, 2015. "Layers of Experiments with Adaptive Combined Design," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(2), pages 127-142, March.
  • Handle: RePEc:wly:navres:v:62:y:2015:i:2:p:127-142
    DOI: 10.1002/nav.21618
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    References listed on IDEAS

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    1. Dasgupta, Tirthankar & Ma, Christopher & Joseph, V. Roshan & Wang, Z.L. & Wu, C. F. Jeff, 2008. "Statistical Modeling and Analysis for Robust Synthesis of Nanostructures," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 594-603, June.
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    Cited by:

    1. Heeyoung Kim & Justin T. Vastola & Sungil Kim & Jye-Chyi Lu & Martha A. Grover, 2017. "Incorporation of engineering knowledge into the modeling process: a local approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5865-5880, October.

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